From private yards to public benefits: How human dimensions shape the urban forest in a small city
Bibliographic record
Abstract
Well managed urban forests provide many benefits, such as decreasing heat island effects, reducing air pollution, and increasing property values. Urban forests are distributed throughout cities, but large portions can be located on private property. Understanding how residents decide to plant and remove trees can inform efforts to spur the growth and protection of urban forests. We surveyed 548 Camrose residents about their tree attitudes and perceptions, environmental attitudes, tree knowledge, and tree planting and removal behaviours. Residents planted 6.0 trees on average, removed 2.7, for a calculated tree net gain of 3.4 on their property. Most tree attitudes and perceptions were positively related to tree planting and tree net gain. Environmental attitudes were not related to any behaviour. Knowledge was positively related to tree planting and removal but not tree net gain. Results also revealed that being male, being older, time living at a property, and owning a home have positive relationships with tree net gain. These findings partially overcome the lack of urban forest studies in small cities, which have received less attention in urban forest literature. The management implications of these findings for the city of Camrose are discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".